code: def test_summarization(self):
      prompt = """summarize: Transfer learning, where a model is first pre-trained on a data-rich task before being 
              fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). 
              The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. 
              In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified 
              framework that converts all text-based language problems into a text-to-text format."""
      output_pytorch = self.generative_t5_pytorch(
          prompt, 100, temperature=0.0
      )[0]
      output_onnx = self.generative_t5_onnx(
          prompt, 100, temperature=0.0
      )[0]
      assert (
          output_pytorch
          == output_onnx
          == "transfer learning is a technique in natural language processing (NLP) it involves pre-trained models before being fine-tuned on downstream tasks. the effectiveness of transfer learning has given rise to a diversity of approaches."
      )
  
file_path: onnxt5/tests/tests_models.py
imports: import unittest
  import torch
  from onnxt5 import create_t5_encoder_decoder, GenerativeT5
  from onnxt5.api import get_encoder_decoder_tokenizer
label_desc: Use image node_id 3 for calling the TestTasks obj's underlying member method code with example usage: obj.test_summarization() without return types